Predicting contemporary and future species distributions is relevant for science and decision making, yet the development of high-resolution spatial predictions for numerous taxonomic groups and regions is limited by the scalability of available modelling tools. Uniting species distribution modelling (SDM) techniques into one high-performance computing (HPC) pipeline, we developed N-SDM, an SDM platform aimed at delivering reproducible outputs for standard biodiversity assessments. N-SDM was built around a spatially-nested framework, intended at facilitating the combined use of species occurrence data retrieved from multiple sources and at various spatial scales. N-SDM allows combining two models fitted with species and covariate data retrieved from global to regional scales, which is useful for addressing the issue of spatial niche truncation. The set of state-of-the-art SDM features embodied in N-SDM includes a newly devised covariate selection procedure, five modelling algorithms, an algorithm-specific hyperparameter grid search, and the ensemble of small-models approach. N-SDM is designed to be run on HPC environments, allowing the parallel processing of thousands of species at the same time. All the information required for installing and running N-SDM is openly available on the GitHub repository https://github.com/N-SDM/N-SDM.

中文翻译：

---

N-SDM：用于嵌套物种分布建模的高性能计算管道

预测当代和未来的物种分布与科学和决策制定相关，但对众多分类群和区域的高分辨率空间预测的发展受到可用建模工具的可扩展性的限制。我们将物种分布建模 (SDM) 技术整合到一个高性能计算 (HPC) 管道中，开发了N-SDM，这是一个旨在为标准生物多样性评估提供可重现输出的 SDM 平台。N-SDM围绕空间嵌套框架构建，旨在促进从多个来源和不同空间尺度检索的物种发生数据的组合使用。N-SDM允许结合从全球到区域尺度检索的物种和协变量数据的两个模型，这对于解决空间生态位截断问题很有用。N-SDM中体现的一组最先进的 SDM 功能包括新设计的协变量选择程序、五种建模算法、特定于算法的超参数网格搜索和小模型方法的集成。N-SDM设计用于在 HPC 环境中运行，允许同时并行处理数千个物种。安装和运行N-SDM所需的所有信息均可在 GitHub 存储库 https://github.com/N-SDM/N-SDM 上公开获得。